Recent global-scale environmental issues from climate change to biodiversity loss are generating an intense social pressure on the scientific community [1]. A growing need for information on environmental topics with appropriate reliability and suitable spatial scalability (from local to global analysis and vice versa) is spreading among societies [2].
The availability of huge amounts of environmental data allows the use of advanced analytic techniques that can provide useful information from a variety of large datasets, including those observing and measuring the ecosystem processes in response to environmental drivers [3]. A multidisciplinary approach, including artificial intelligence, big data analytics, and ecological modelling, is highly recommended to interpret ecological processes and identify adequate solutions for the environmental issues of the Anthropocene [4]. However, the use of big data today generated by different sources represents a big challenge, from detailed analysis on specific topics or geographic areas to issues at wider scales and over broader timescales [5].
Earth Observation (EO) data acquired by satellite sensors offer new opportunities for the ecology sciences and are revolutionizing the methodologies applied, from experimental/theoretical to computational science [6], projecting big data from space in the mainstream of ecological analysis.
It is therefore easily foreseeable that, in the next decades, new technologies will affect the activities on ecosystem survey, mapping, and monitoring, opening a new era. The reasons are first linked to the requirements of global, continental, and national policies on the environment sustainability, such as those stated in the 2030 Agenda for Sustainable Development, that gave a new stimulus to improve ecological research in this direction [7,8]. The increasing demand from national institutions for updated information to monitor ecosystems and detect their changes in time and space plays a crucial role in demonstrating mapping products as an essential tool for biodiversity assessments [9]. Indeed, in the light of “Biological Diversity” concept (see Convention on Biological Diversity: https://www.cbd.int/convention/text/ (accessed on 1 May 2022)), habitats are cardinal pieces for quantitative estimations of biodiversity at local and global scales. They are basic units of ecosystems and biomes identified by abiotic environmental factors, such as climate, geomorphology, pedology, as well as by plant species composition (i.e., vegetation units) [10].
In this direction, this Special Issue aims to compile research papers dealing with both methodologies of remote sensing and implementation of research results to facilitate the environmental monitoring, using geospatial techniques, in several ecosystems (e.g., wetland, coastal, estuarine, forest, shrubland, and alpine grasslands) or for land use and land cover (LULC) changes analysis. Altogether, in this Special Issue, nine papers are published, and the results obtained are implemented along two continents, using remote sensing platforms such as Landsat (i.e., 5TM, 7ETM+ and 8OLI), Sentinel (2A/2B MSI), World-View, and SPOT 5 imageries or hyperspectral imagery from proximal sensors by airborne vehicles (i.e., helicopter). Among the methods used to process the remotely sensed data, the increasing focus on the use of machine learning algorithm models such as Random Forests (RF), Support Vector Machine (SVM), Linear Regression (LR), Convolutional Neural Network (CNN), and Deep Learning (DL) classifier is noteworthy. In Table 1, the key message of all published papers is summarized. More detailed information on each article published in this Special Issue is given below in order of the publication date.
